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Data X:
14.2754 0 12.5621 13.3181 16.8622 25.2609 12.2961 0 14.2754 12.5621 13.3181 16.8622 10.0871 0 12.2961 14.2754 12.5621 13.3181 13.5117 0 10.0871 12.2961 14.2754 12.5621 13.9921 0 13.5117 10.0871 12.2961 14.2754 13.6932 0 13.9921 13.5117 10.0871 12.2961 14.4211 0 13.6932 13.9921 13.5117 10.0871 15.3397 0 14.4211 13.6932 13.9921 13.5117 16.5182 0 15.3397 14.4211 13.6932 13.9921 15.2809 0 16.5182 15.3397 14.4211 13.6932 15.6204 0 15.2809 16.5182 15.3397 14.4211 15.5698 0 15.6204 15.2809 16.5182 15.3397 15.9458 0 15.5698 15.6204 15.2809 16.5182 16.4063 0 15.9458 15.5698 15.6204 15.2809 17.55 0 16.4063 15.9458 15.5698 15.6204 17.0353 0 17.55 16.4063 15.9458 15.5698 16.0591 0 17.0353 17.55 16.4063 15.9458 16.3643 0 16.0591 17.0353 17.55 16.4063 14.6527 0 16.3643 16.0591 17.0353 17.55 13.4664 0 14.6527 16.3643 16.0591 17.0353 13.3266 0 13.4664 14.6527 16.3643 16.0591 13.1823 0 13.3266 13.4664 14.6527 16.3643 12.113 0 13.1823 13.3266 13.4664 14.6527 13.354 0 12.113 13.1823 13.3266 13.4664 13.4537 0 13.354 12.113 13.1823 13.3266 13.2715 0 13.4537 13.354 12.113 13.1823 13.1959 0 13.2715 13.4537 13.354 12.113 13.5542 0 13.1959 13.2715 13.4537 13.354 12.124 0 13.5542 13.1959 13.2715 13.4537 10.967 0 12.124 13.5542 13.1959 13.2715 10.9201 0 10.967 12.124 13.5542 13.1959 12.5971 0 10.9201 10.967 12.124 13.5542 14.3177 0 12.5971 10.9201 10.967 12.124 14.2471 0 14.3177 12.5971 10.9201 10.967 16.0926 0 14.2471 14.3177 12.5971 10.9201 17.1334 0 16.0926 14.2471 14.3177 12.5971 16.5866 0 17.1334 16.0926 14.2471 14.3177 16.361 0 16.5866 17.1334 16.0926 14.2471 15.8494 0 16.361 16.5866 17.1334 16.0926 15.5932 0 15.8494 16.361 16.5866 17.1334 16.6387 0 15.5932 15.8494 16.361 16.5866 16.8312 0 16.6387 15.5932 15.8494 16.361 16.5044 0 16.8312 16.6387 15.5932 15.8494 16.5556 0 16.5044 16.8312 16.6387 15.5932 16.7469 0 16.5556 16.5044 16.8312 16.6387 15.9543 0 16.7469 16.5556 16.5044 16.8312 15.5888 0 15.9543 16.7469 16.5556 16.5044 14.3945 0 15.5888 15.9543 16.7469 16.5556 13.8889 0 14.3945 15.5888 15.9543 16.7469 12.9999 0 13.8889 14.3945 15.5888 15.9543 14.1022 0 12.9999 13.8889 14.3945 15.5888 19.6245 0 14.1022 12.9999 13.8889 14.3945 24.7658 0 19.6245 14.1022 12.9999 13.8889 25.9843 0 24.7658 19.6245 14.1022 12.9999 22.9635 0 25.9843 24.7658 19.6245 14.1022 19.6288 0 22.9635 25.9843 24.7658 19.6245 17.3363 0 19.6288 22.9635 25.9843 24.7658 13.311 0 17.3363 19.6288 22.9635 25.9843 14.6359 0 13.311 17.3363 19.6288 22.9635 15.834 0 14.6359 13.311 17.3363 19.6288 16.2415 0 15.834 14.6359 13.311 17.3363 15.9808 0 16.2415 15.834 14.6359 13.311 16.9726 0 15.9808 16.2415 15.834 14.6359 16.8708 0 16.9726 15.9808 16.2415 15.834 16.923 0 16.8708 16.9726 15.9808 16.2415 18.1138 0 16.923 16.8708 16.9726 15.9808 16.7716 0 18.1138 16.923 16.8708 16.9726 14.0299 0 16.7716 18.1138 16.923 16.8708 13.822 0 14.0299 16.7716 18.1138 16.923 14.2537 0 13.822 14.0299 16.7716 18.1138 14.3985 0 14.2537 13.822 14.0299 16.7716 15.2454 0 14.3985 14.2537 13.822 14.0299 15.6683 0 15.2454 14.3985 14.2537 13.822 16.1721 0 15.6683 15.2454 14.3985 14.2537 14.8679 0 16.1721 15.6683 15.2454 14.3985 14.1948 0 14.8679 16.1721 15.6683 15.2454 14.7056 0 14.1948 14.8679 16.1721 15.6683 15.3819 0 14.7056 14.1948 14.8679 16.1721 15.5001 0 15.3819 14.7056 14.1948 14.8679 14.7886 0 15.5001 15.3819 14.7056 14.1948 14.563 0 14.7886 15.5001 15.3819 14.7056 15.5528 0 14.563 14.7886 15.5001 15.3819 15.9781 0 15.5528 14.563 14.7886 15.5001 15.5139 0 15.9781 15.5528 14.563 14.7886 15.3603 0 15.5139 15.9781 15.5528 14.563 15.0512 0 15.3603 15.5139 15.9781 15.5528 14.7874 0 15.0512 15.3603 15.5139 15.9781 14.9624 0 14.7874 15.0512 15.3603 15.5139 13.9188 0 14.9624 14.7874 15.0512 15.3603 14.5146 0 13.9188 14.9624 14.7874 15.0512 13.7115 0 14.5146 13.9188 14.9624 14.7874 12.0738 0 13.7115 14.5146 13.9188 14.9624 12.5688 0 12.0738 13.7115 14.5146 13.9188 12.2547 0 12.5688 12.0738 13.7115 14.5146 11.8741 0 12.2547 12.5688 12.0738 13.7115 13.0261 0 11.8741 12.2547 12.5688 12.0738 13.8681 0 13.0261 11.8741 12.2547 12.5688 14.2137 0 13.8681 13.0261 11.8741 12.2547 14.4743 0 14.2137 13.8681 13.0261 11.8741 13.9764 0 14.4743 14.2137 13.8681 13.0261 13.1558 0 13.9764 14.4743 14.2137 13.8681 13.0991 0 13.1558 13.9764 14.4743 14.2137 13.7831 0 13.0991 13.1558 13.9764 14.4743 13.2546 0 13.7831 13.0991 13.1558 13.9764 13.3426 0 13.2546 13.7831 13.0991 13.1558 13.5011 0 13.3426 13.2546 13.7831 13.0991 12.8245 0 13.5011 13.3426 13.2546 13.7831 13.6596 0 12.8245 13.5011 13.3426 13.2546 13.8754 0 13.6596 12.8245 13.5011 13.3426 12.9011 0 13.8754 13.6596 12.8245 13.5011 11.871 0 12.9011 13.8754 13.6596 12.8245 12.3954 0 11.871 12.9011 13.8754 13.6596 12.8179 0 12.3954 11.871 12.9011 13.8754 12.1219 0 12.8179 12.3954 11.871 12.9011 12.6176 0 12.1219 12.8179 12.3954 11.871 13.6362 0 12.6176 12.1219 12.8179 12.3954 13.5422 0 13.6362 12.6176 12.1219 12.8179 13.362 0 13.5422 13.6362 12.6176 12.1219 14.5735 0 13.362 13.5422 13.6362 12.6176 15.8357 0 14.5735 13.362 13.5422 13.6362 14.9927 0 15.8357 14.5735 13.362 13.5422 14.5078 0 14.9927 15.8357 14.5735 13.362 15.2648 0 14.5078 14.9927 15.8357 14.5735 15.7163 0 15.2648 14.5078 14.9927 15.8357 17.7969 0 15.7163 15.2648 14.5078 14.9927 19.0408 0 17.7969 15.7163 15.2648 14.5078 17.8571 0 19.0408 17.7969 15.7163 15.2648 18.815 0 17.8571 19.0408 17.7969 15.7163 19.0961 0 18.815 17.8571 19.0408 17.7969 17.6215 0 19.0961 18.815 17.8571 19.0408 17.0163 0 17.6215 19.0961 18.815 17.8571 15.8286 0 17.0163 17.6215 19.0961 18.815 16.7818 0 15.8286 17.0163 17.6215 19.0961 15.8726 0 16.7818 15.8286 17.0163 17.6215 16.6621 0 15.8726 16.7818 15.8286 17.0163 17.5709 0 16.6621 15.8726 16.7818 15.8286 16.9914 0 17.5709 16.6621 15.8726 16.7818 18.0412 0 16.9914 17.5709 16.6621 15.8726 16.9764 0 18.0412 16.9914 17.5709 16.6621 15.7649 0 16.9764 18.0412 16.9914 17.5709 14.3928 0 15.7649 16.9764 18.0412 16.9914 13.5061 0 14.3928 15.7649 16.9764 18.0412 12.7433 0 13.5061 14.3928 15.7649 16.9764 13.017 0 12.7433 13.5061 14.3928 15.7649 13.0171 0 13.017 12.7433 13.5061 14.3928 12.2412 0 13.0171 13.017 12.7433 13.5061 11.8878 0 12.2412 13.0171 13.017 12.7433 11.2511 0 11.8878 12.2412 13.0171 13.017 11.8583 0 11.2511 11.8878 12.2412 13.0171 11.1202 0 11.8583 11.2511 11.8878 12.2412 10.185 0 11.1202 11.8583 11.2511 11.8878 8.7563 0 10.185 11.1202 11.8583 11.2511 9.5267 0 8.7563 10.185 11.1202 11.8583 9.4106 0 9.5267 8.7563 10.185 11.1202 11.878 0 9.4106 9.5267 8.7563 10.185 14.4228 0 11.878 9.4106 9.5267 8.7563 14.896 0 14.4228 11.878 9.4106 9.5267 15.6664 0 14.896 14.4228 11.878 9.4106 18.147 0 15.6664 14.896 14.4228 11.878 19.3069 0 18.147 15.6664 14.896 14.4228 21.6807 0 19.3069 18.147 15.6664 14.896 20.7934 0 21.6807 19.3069 18.147 15.6664 23.4241 0 20.7934 21.6807 19.3069 18.147 24.8273 0 23.4241 20.7934 21.6807 19.3069 24.9276 0 24.8273 23.4241 20.7934 21.6807 27.4256 0 24.9276 24.8273 23.4241 20.7934 28.1746 0 27.4256 24.9276 24.8273 23.4241 24.5615 0 28.1746 27.4256 24.9276 24.8273 30.2532 0 24.5615 28.1746 27.4256 24.9276 31.2514 0 30.2532 24.5615 28.1746 27.4256 30.4733 0 31.2514 30.2532 24.5615 28.1746 33.3047 0 30.4733 31.2514 30.2532 24.5615 37.2103 0 33.3047 30.4733 31.2514 30.2532 36.7711 0 37.2103 33.3047 30.4733 31.2514 37.7163 0 36.7711 37.2103 33.3047 30.4733 28.8488 0 37.7163 36.7711 37.2103 33.3047 27.4682 0 28.8488 37.7163 36.7711 37.2103 29.8793 0 27.4682 28.8488 37.7163 36.7711 28.0598 0 29.8793 27.4682 28.8488 37.7163 29.7733 0 28.0598 29.8793 27.4682 28.8488 32.6926 0 29.7733 28.0598 29.8793 27.4682 32.4803 0 32.6926 29.7733 28.0598 29.8793 29.4168 0 32.4803 32.6926 29.7733 28.0598 28.7054 0 29.4168 32.4803 32.6926 29.7733 28.7614 0 28.7054 29.4168 32.4803 32.6926 23.8075 0 28.7614 28.7054 29.4168 32.4803 21.6987 0 23.8075 28.7614 28.7054 29.4168 21.4691 0 21.6987 23.8075 28.7614 28.7054 22.5709 0 21.4691 21.6987 23.8075 28.7614 23.4546 0 22.5709 21.4691 21.6987 23.8075 27.8976 0 23.4546 22.5709 21.4691 21.6987 29.2965 0 27.8976 23.4546 22.5709 21.4691 28.1191 0 29.2965 27.8976 23.4546 22.5709 25.812 0 28.1191 29.2965 27.8976 23.4546 25.931 0 25.812 28.1191 29.2965 27.8976 26.9925 0 25.931 25.812 28.1191 29.2965 28.9213 0 26.9925 25.931 25.812 28.1191 27.8898 0 28.9213 26.9925 25.931 25.812 24.2473 0 27.8898 28.9213 26.9925 25.931 27.1056 0 24.2473 27.8898 28.9213 26.9925 28.2833 0 27.1056 24.2473 27.8898 28.9213 29.8076 0 28.2833 27.1056 24.2473 27.8898 27.1826 0 29.8076 28.2833 27.1056 24.2473 22.8764 0 27.1826 29.8076 28.2833 27.1056 21.938 0 22.8764 27.1826 29.8076 28.2833 23.3076 0 21.938 22.8764 27.1826 29.8076 24.9572 0 23.3076 21.938 22.8764 27.1826 26.4694 0 24.9572 23.3076 21.938 22.8764 23.9297 0 26.4694 24.9572 23.3076 21.938 24.7033 0 23.9297 26.4694 24.9572 23.3076 24.646 0 24.7033 23.9297 26.4694 24.9572 24.0496 0 24.646 24.7033 23.9297 26.4694 24.2096 0 24.0496 24.646 24.7033 23.9297 24.0717 0 24.2096 24.0496 24.646 24.7033 26.6673 0 24.0717 24.2096 24.0496 24.646 27.6457 0 26.6673 24.0717 24.2096 24.0496 30.8791 0 27.6457 26.6673 24.0717 24.2096 29.3278 0 30.8791 27.6457 26.6673 24.0717 30.7268 0 29.3278 30.8791 27.6457 26.6673 34.1204 0 30.7268 29.3278 30.8791 27.6457 35.0205 0 34.1204 30.7268 29.3278 30.8791 39.3565 0 35.0205 34.1204 30.7268 29.3278 34.4724 0 39.3565 35.0205 34.1204 30.7268 29.9762 0 34.4724 39.3565 35.0205 34.1204 33.6008 0 29.9762 34.4724 39.3565 35.0205 35.2464 0 33.6008 29.9762 34.4724 39.3565 40.4137 0 35.2464 33.6008 29.9762 34.4724 41.3922 0 40.4137 35.2464 33.6008 29.9762 39.4243 0 41.3922 40.4137 35.2464 33.6008 45.7259 0 39.4243 41.3922 40.4137 35.2464 48.2549 0 45.7259 39.4243 41.3922 40.4137 52.0461 0 48.2549 45.7259 39.4243 41.3922 52.1871 0 52.0461 48.2549 45.7259 39.4243 49.3474 0 52.1871 52.0461 48.2549 45.7259 47.8653 0 49.3474 52.1871 52.0461 48.2549 48.5179 0 47.8653 49.3474 52.1871 52.0461 52.4815 0 48.5179 47.8653 49.3474 52.1871 51.8171 0 52.4815 48.5179 47.8653 49.3474 52.5811 0 51.8171 52.4815 48.5179 47.8653 57.5617 0 52.5811 51.8171 52.4815 48.5179 55.7091 0 57.5617 52.5811 51.8171 52.4815 55.4378 0 55.7091 57.5617 52.5811 51.8171 58.7493 0 55.4378 55.7091 57.5617 52.5811 57.794 0 58.7493 55.4378 55.7091 57.5617 50.282 0 57.794 58.7493 55.4378 55.7091 47.6976 0 50.282 57.794 58.7493 55.4378 46.7381 0 47.6976 50.282 57.794 58.7493 47.4282 0 46.7381 47.6976 50.282 57.794 42.2269 0 47.4282 46.7381 47.6976 50.282 44.9066 0 42.2269 47.4282 46.7381 47.6976 47.2648 0 44.9066 42.2269 47.4282 46.7381 50.2325 0 47.2648 44.9066 42.2269 47.4282 50.2504 0 50.2325 47.2648 44.9066 42.2269 52.5685 0 50.2504 50.2325 47.2648 44.9066 55.2325 0 52.5685 50.2504 50.2325 47.2648 52.3674 0 55.2325 52.5685 50.2504 50.2325 55.1692 0 52.3674 55.2325 52.5685 50.2504 57.7252 0 55.1692 52.3674 55.2325 52.5685 62.8232 0 57.7252 55.1692 52.3674 55.2325 62.7599 0 62.8232 57.7252 55.1692 52.3674 62.4387 0 62.7599 62.8232 57.7252 55.1692 64.0862 1 62.4387 62.7599 62.8232 57.7252 66.1209 1 64.0862 62.4387 62.7599 62.8232 69.8474 1 66.1209 64.0862 62.4387 62.7599 80.1039 1 69.8474 66.1209 64.0862 62.4387 85.9319 1 80.1039 69.8474 66.1209 64.0862 85.2843 1 85.9319 80.1039 69.8474 66.1209 77.0383 1 85.2843 85.9319 80.1039 69.8474 69.9981 1 77.0383 85.2843 85.9319 80.1039 55.2039 1 69.9981 77.0383 85.2843 85.9319 43.1188 1 55.2039 69.9981 77.0383 85.2843 32.077 1 43.1188 55.2039 69.9981 77.0383 34.2974 1 32.077 43.1188 55.2039 69.9981 34.5613 1 34.2974 32.077 43.1188 55.2039 36.5235 1 34.5613 34.2974 32.077 43.1188 39.0474 1 36.5235 34.5613 34.2974 32.077 42.8033 1 39.0474 36.5235 34.5613 34.2974 49.5164 1 42.8033 39.0474 36.5235 34.5613 46.459 0 49.5164 42.8033 39.0474 36.5235 51.1313 0 46.459 49.5164 42.8033 39.0474 46.9331 0 51.1313 46.459 49.5164 42.8033 49.7654 0 46.9331 51.1313 46.459 49.5164 52.0729 0 49.7654 46.9331 51.1313 46.459 51.6425 0 52.0729 49.7654 46.9331 51.1313 53.9784 0 51.6425 52.0729 49.7654 46.9331 54.4891 0 53.9784 51.6425 52.0729 49.7654 59.0665 0 54.4891 53.9784 51.6425 52.0729 63.9929 0 59.0665 54.4891 53.9784 51.6425 61.6167 0 63.9929 59.0665 54.4891 53.9784 62.1816 0 61.6167 63.9929 59.0665 54.4891 58.9178 0 62.1816 61.6167 63.9929 59.0665 59.9151 0 58.9178 62.1816 61.6167 63.9929
Names of X columns:
Prijs Crisis 1 2 3 4
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Monthly Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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